Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species.

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

Screening the baseline fish bioconcentration factor of various types of surfactants using phospholipid binding data.

Fish bioconcentration factors (BCFs) are commonly used in chemical hazard and risk assessment. For neutral organic chemicals BCFs are positively correlated with the octanol-water partition ratio (KOW), but KOW is not a reliable parameter for surfactants. Membrane lipid-water distribution ratios (DMLW) can be accurately measured for all kinds of surfactants, using phospholipid-based sorbents. This study first demonstrates that DMLW values for ionic surfactants are more than 100 000 times higher than the partition ratio to fish-oil, representing neutral storage lipid. A non-ionic alcohol ethoxylate surfactant showed almost equal affinity for both lipid types. Accordingly, a baseline screening BCF value for surfactants (BCFbaseline) can be approximated for ionic surfactants by multiplying DMLW by the phospholipid fraction in tissue, and for non-ionic surfactants by multiplying DMLW by the total lipid fraction. We measured DMLW values for surfactant structures, including linear and branched alkylbenzenesulfonates, an alkylsulfoacetate and an alkylethersulfate, bis(2-ethylhexyl)-surfactants (e.g., docusate), zwitterionic alkylbetaines and alkylamine-oxides, and a polyprotic diamine. Together with sixty previously published DMLW values for surfactants, structure-activity relationships were derived to elucidate the influence of surfactant specific molecular features on DMLW. For 23 surfactant types, we established the alkyl chain length at which BCFbaseline would exceed the EU REACH bioaccumulation (B) threshold of 2000 L kg-1, and would therefore require higher tier assessments to further refine the BCF estimate. Finally, the derived BCFbaseline are compared with measured literature in vivo BCF data where available, suggesting that refinements, most notably reliable estimates of biotransformation rates, are needed for most surfactant types.

Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies With Different Degrees of Completeness and Scientific Confidence.

Adverse outcome pathways (AOPs) offer a pathway-based toxicological framework to support hazard assessment and regulatory decision-making. However, little has been discussed about the scientific confidence needed, or how complete a pathway should be, before use in a specific regulatory application. Here we review four case studies to explore the degree of scientific confidence and extent of completeness (in terms of causal events) that is required for an AOP to be useful for a specific purpose in a regulatory application: (i) Membrane disruption (Narcosis) leading to respiratory failure (low confidence), (ii) Hepatocellular proliferation leading to cancer (partial pathway, moderate confidence), (iii) Covalent binding to proteins leading to skin sensitization (high confidence), and (iv) Aromatase inhibition leading to reproductive dysfunction in fish (high confidence). Partially complete AOPs with unknown molecular initiating events, such as 'Hepatocellular proliferation leading to cancer', were found to be valuable. We demonstrate that scientific confidence in these pathways can be increased though the use of unconventional information (eg, computational identification of potential initiators). AOPs at all levels of confidence can contribute to specific uses. A significant statistical or quantitative relationship between events and/or the adverse outcome relationships is a common characteristic of AOPs, both incomplete and complete, that have specific regulatory uses. For AOPs to be useful in a regulatory context they must be at least as useful as the tools that regulators currently possess, or the techniques currently employed by regulators.